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machine learning Implementations in Childhood Stunting Research: A Systematic Literature Review 8

Machine Learning Implementations in Childhood Stunting Resea...

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8th international conference on Information Management and Technology, ICIMTech 2023

作者： Rahutomo, Reza Elwirehardja, Gregorius Natanael Isnan, Mahmud Asadi, Faisal Pardamean, Bens School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Binus Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

Childhood stunting is a condition anticipated to affect the growth potential of children under the age of five. With numerous stunting researches that have been conducted, stunting datasets are now widely available to facilitate stunting research. this provides an opportunity to implement machine learning (ML) principles to produce a broader insight or a novel technique in stunting prediction. A systematic literature review is necessary to discover the landscape of machine learning implementation in the application domain as a preliminary study for creating an effective research roadmap. this paper presents a systematic literature review (SLR) of 22 curated manuscripts that focuses on identifying the ML models applied in stunting research, as well as the datasets used in such studies that were published during 2017-2022. the SLR process found that ML principles have been applied in stunting research since 2017, and the diversity of ML implementation has become more varied in 2021-2022. In terms of ML models, XGBoost and Random Forest are recognized as the two most utilized models, and stunting prediction is the most common ML implementation. the majority of stunting research utilizing ML has been conducted in Indonesia. Although national survey data has been the most commonly utilized dataset in stunting research, researchers in Indonesia have shown a preference for utilizing data from regional or independent surveys. this study will be followed by developing a classifier model for stunted children using XGBoost and Random Forest algorithms. the model will be trained on a dataset generated from StuntingDB. © 2023 IEEE.

关键词： machine learning

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Elevating Aloe Vera Leaf Disease Detection through Federated learning CNNs 4

Elevating Aloe Vera Leaf Disease Detection through Federated...

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4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Sharma, Vikrant Kukreja, Vinay Mehta, Shiva Garg, Navin Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Chitkara University Institute Of Engineering And Technology Chitkara University Punjab India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350384628

this study explores the novel use of federated learning in conjunction with CNN for illness classification in Aloe vera leaves. Five separate client-contributed classes of Aloe Vera leaf diseases form the basis of the dataset used in the research. the technique is centered on using federated learning to create a decentralized data analysis system that protects privacy. Each client's local datasets are used to create a model that is optimized worldwide. the examination of the results included a thorough evaluation of the model's performance across all five customers (nh-1 to nh-5) using a variety of statistical metrics. To provide a complete picture of the model's performance, compute the macro, micro, and weighted averages. Results showed that nh-1, nh-2, nh-3, nh-4, and nh-5 all had macro standards of 88.61%, 84.07%, 80.08%, 90.02%, and 94.16%, respectively, indicating that the model performed evenly across all classes. Using weighted averages, which take into account the class imbalance, further analysis revealed that nh-5 was in the lead at 94.25%, followed by nh-4 at 90.04%, nh-1 at 88.97%, nh-2 at 84.45%, and nh-3 at 80.13%. Micro averages, which take into account all classes' contributions, con firmed similar findings: NH 5 achieved 94.25%, NH 4 gained 90.04%, NH 1 reached 88.94%, NH 2 achieved 84.49%, and NH 3 achieved 80.15%. In particular, for agricultural disease identification, the study's major contribution is the way it shows how federated learning may be used to merge local data into a strong global model. © 2023 IEEE.

关键词： Data privacy

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Android Malware Detection Using Deep learning Techniques 4

Android Malware Detection Using Deep Learning Techniques

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4th international conference on Multimedia Processing, Communication and Information Technology, MPCIT 2024

作者： Janardhana, D.R. Nithin, H.V. Sandhya, B.R. Bhatia, Aakash D. Nitte Meenakshi Institute of Technology Department of CSE Bengaluru India PES Institute of Technology & Management Department of Data Science Shivamogga India

ISBN: (纸本)9798350375466

With the rapid increase in Android devices and applications, securing these platforms has become critical. Android malware presents serious risks to user privacy, data integrity, and device functionality. Traditional malware detection methods are often insufficient due to the evolving sophistication of modern malware. this study investigates the use of deep learning, specifically Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for enhancing Android malware detection accuracy and robustness. In this work, there is a comprehensive framework that utilizes both static and dynamic analysis to extract extensive features from Android applications. Static analysis examines the application's code, permissions, and metadata without executing the app, while dynamic analysis observes the app's behavior during runtime. these features are fed into CNN and RNN models, which are adept at handling spatial and sequential data, respectively, capturing complex patterns and temporal dependencies in malware behavior. Extensive experiments on a benchmark dataset of benign and malicious Android applications show that machine learning models, particularly CNNs and RNNs, significantly outperform traditional signature-based detection techniques. In this work, the best-performing model achieved a detection accuracy of over 95% with a low false-positive rate, demonstrating the potential of CNNs and RNNs to provide robust and scalable solutions for Android malware detection. this research highlights the importance of continuous feature engineering and model updating to adapt to evolving malware tactics and suggests that hybrid approaches combining static and dynamic analysis can offer more comprehensive defense mechanisms against sophisticated threats. Future work will focus on real-time detection capabilities and integrating these models into practical security solutions for end-users. © 2024 IEEE.

关键词： Android malware

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Research on intelligent customs declaration generation in Guangdong-Hong Kong cross-border road cargo clearance 4

Research on intelligent customs declaration generation in Gu...

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4th international conference on computer Science and Communication Technology, ICCSCT 2023

作者： Han, Chengguo Wang, Bin School of Management Guangdong University of Science and Technology Guangdong Dongguan523083 China Shenzhen Taizhou Technology Co. Ltd. Guangdong Shenzhen518031 China

ISBN: (纸本)9781510671232

Due to the uniqueness of "one country, two systems" and "two customs territories" in Guangdong (GD) and Hong Kong (HK), cross-border road cargo clearance between the two regions requires the creation of several documents. these include the Road Cargo Manifest, Vehicle Declaration, Customs Declaration, HK Seamless, HK Bundle, and HK Customs Declaration, as well as additional documents such as Packing List, Contract, and more. the use of Artificial intelligence (AI) can streamline the process of generating these documents and ensure accuracy and efficiency in cross-border road cargo clearance operations. this paper focuses on four aspects: text scanning and recognition, extraction of commodity classification declaration elements, conversion of customs declaration documents, and intelligent commodity classification. these areas of research aim to address the time-consuming and labour-intensive process of document creation and verification. By leveraging AI technologies, such as optical character recognition (OCR), data extraction algorithms, and machine learning (ML), it is possible to automate and optimize the production and review of these documents, thereby reducing time and manpower costs in cross-border road cargo clearance operations between GD and HK. Based on big data analysis of customs declaration documents from both regions, AI technologies such as feature extraction and machine learning can be utilized to achieve automation in the identification of documents like road cargo manifests and customs declarations. this research applied to the China international Trade Single Window (Shenzhen) and significantly improved the efficiency and accuracy of customs declaration in GD and HK. © 2023 SPIE.

关键词： international trade

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the Analysis and application of Face Recognition Technology 4

The Analysis and Application of Face Recognition Technology

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4th international conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Jiang, Nan Shandong University of Technology Zibo China

ISBN: (纸本)9798350342710

At present, face recognition technology is developing rapidly, it has a broad future prospect and many scholars have formed some scientific research results, which makes the technology develop rapidly in various industries. By reviewing several literatures, there is still a lack of relevant research that elaborates on face recognition technology, so this paper will be based on an in-depth study of face recognition technology. this research will provide a comprehensive analysis of face recognition systems, their main components and principles, some examples of face recognition algorithms and an analysis of their advantages and disadvantages, and a comprehensive analysis of the problems that exist at this stage of the technology. the article concludes with a summary and outlook of the whole paper. © 2023 IEEE.

关键词： Face recognition

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Extended Optimal Control Problem for Practical application 4th

Extended Optimal Control Problem for Practical Application

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Diveev, Askhat Shmalko, Elizaveta Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9783031812408

To apply a solution of the optimal control problem directly to the control object for which model this problem was solved, it is necessary to build a system of motion stabilization along the obtained optimal trajectory. However, the construction of such a stabilization system is not provided in the classical formulation of the optimal control problem. therefore, additional requirements for the properties of the optimal solution have been introduced into the optimal control problem. A formulation of the extended optimal control problem is introduced and the approaches to its numerical solution are discussed. One way to meet the introduced requirements is to build a stabilization motion system of an object along a program trajectory. As far as stabilization to the whole program trajectory, not from point to point on this trajectory, is rather challenging task, so for this, numerical methods of symbolic regression are proposed to be applied. the approach consists in application of machine learning by symbolic regression to the control synthesis problem. Symbolic regression allows to find a mathematical expression for control function as a function of the state space vector. Computational example of solving the extended optimal control problem for a robot group is presented. It is shown experimentally, that the solution of the extended optimal control problem is much less sensitive to disturbances, than a direct solution of the classical optimal control problem. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Stabilization

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An improved triangular management framework for improve the automotive vehicles using decision based data analysis 4

An improved triangular management framework for improve the ...

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4th international conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Sharma, Sanjeev Vashist, Prachi Sinha, Tanu Siemens India Pvt Ltd Automotive Business Unit UP Noida India Raj Kumar Goel Institute of Technology Department of Information Technology UP Ghaziabad India Abes Institute of Technology Department of Computer and Application UP Ghaziabad India

ISBN: (纸本)9798350364729

the automotive industry changes quickly due to incredible new technologies and client demands. Automotive manufacturers must constantly push the envelope to improve their vehicles to be competitive. In practice, this is easier said than done - it demands near-impossible management and decision-making throughout all levels of the organization. To handle this, we put forward a novel decision-driven data triangulation framework. Data Collection and Analysis A framework consists of data collection, decision-making, and implementation. Component 1: - Here, data is collected from different sources like customer feedback, market trends and internal processes. machine learning and predictive analytics are all employed to sift out in the data what it is that's valuable. the decision-making component then uses this data to make informed decisions and set direction for improvement. this involves establishing a sense of the areas most important for change and detailing the exact process directing change. the third component is executing these decisions, continually monitoring and iterating to determine a better decision. Fused with sound decision-making and implementation requirements, our framework empowers automotive manufacturers to drive tangible vehicle improvement through data-driven decisions. It helps manufacturers maintain fast development in the automotive market and improves customer satisfaction and loyalty. © 2024 IEEE.

关键词： Commerce

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Abstractive Text Summarisation Using Keywords with Transformers Model 8

Abstractive Text Summarisation Using Keywords with Transform...

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8th IEEE international conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2023

作者： Shanmugam S, P. Mithul Sudharsan, R. Vignesh, P.S. Ashwin, S.S. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350347791

the application of linguistics to machine learning models has changed text summarization techniques this abstractive method is used to give the best possible summary of huge data without losing the important key points in it. We will be using the BERT encoder for encoding the input text and collecting the vector of the important sentence in the paragraph and that vector will be sent to Transformers for decoding into a text. this text will be created abstractedly using the transform decoder model. For a long time, automatic text summarization has been the focus of natural language processing (NLP) research. the goal of abstractive text summarising is to produce a summary of a given document that only includes the most crucial and pertinent information. Among deep learning models, BERT and transformer models have dominated abstractive text summarization in recent years. this work offers a thorough analysis of transformer-based and BERT abstractive text summarization. We also go through how these models are developed and trained, as well as the many methods for bringing context and subject-matter expertise into the summarising procedure. © 2023 IEEE.

关键词： Deep learning

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PartSeg v2: Bioimage Segmentation Using Advanced Deep learning Techniques 4th

PartSeg v2: Bioimage Segmentation Using Advanced Deep Learni...

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4th international conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Bokota, Grzegorz Basu, Subhadip Sroka, Jacek Insititute of Informatics University of Warsaw Warsaw Poland Department of Computer Science and Engineering Jadavpur University Kolkata India

ISBN: (纸本)9789819726134

In this paper, we present a new version of our bioimaging tool PartSeg. It allows integration of deep learning models from the Bioimage Model Zoo, which is a community-driven AI model repository. We also show how PartSeg’s power users can train such models, for example, to increase automatization of their data processing workflows. then, we present further new features of PartSeg that make it easier to publish datasets in a durable and referencable way with version tracking. We also describe good practices related to data publishing and how adding metadata can allow for easier data discovery. Availability of such published datasets enables development of even better machine learning models. Publishing of the data is also a requirement for verifiable and repeatable science, leads to greater visibility of the results and enables cooperation and cross-domain exchange of ideas. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bioimaging

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machine Translation of literary texts: genres, times and systems 1

Machine Translation of literary texts: genres, times and sys...

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1st Workshop on Natural Language Processing Tools and Resources for Translation and Interpreting applications, NLP4TIA 2023

作者： Cespedosa, Ana Isabel Mitkov, Ruslan Lancaster University United Kingdom

ISBN: (纸本)9789544520915

machine Translation (MT) has taken off dramatically in recent years due to the advent of Deep learning methods and Neural machine Translation (NMT) has enhanced the quality of automatic translation significantly. While most work has covered the automatic translation of technical, legal and medical texts, the application of MT to literary texts and the human role in this process have been underexplored. In an effort to bridge the gap of this under-researched area, this paper presents the results of a study which seeks to evaluate the performance of three MT systems applied to two different literary genres, two novels (1984 by George Orwell and Pride and Prejudice by Jane Austen) and two poems (I Felt a Funeral in my Brain by Emily Dickinson and Siren Song by Margaret Atwood) representing different literary periods and timelines. the evaluation was conducted by way of the automatic evaluation metric BLEU to objectively assess the performance that the MT system shows on each genre. the limitations of this study are also outlined. © NLP4TIA 2023 - 1st Workshop on Natural Language Processing Tools and Resources for Translation and Interpreting applications, associated with the 14th international conference on Recent Advances in Natural Language Processing, RANLP 2023 - proceedings.

关键词： Deep learning

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